Some Bayesian approaches have been developed for combined classification and clustering, e.g., [1]}, [2]} but they are computationally expensive.
To the best of our knowledge, closest and most recent work in spirit and purpose to our work
is Deep Mixture Neural Network (DMNN) [3]}.
The Deep Mixture Neural Network (DMNN), consisting of an embedding network with gating and a user-defined number of local predictive networks proposes simultaneous patient subtyping and risk prediction. The gating network computes high-level feature representations of raw input features, which are then clustered to find patient cohorts. Local predictor networks are then trained to predict the outcomes for their respective cohorts. The subgroup-specific sets of features learned by the local features provide valuable information from which we can reason about the cluster phenotype.
Thus, DMNN represents `cluster-then-predict' type of approach as it uses local predictor networks to classify subpopulations in the data.
